Volume 12, Number 23, December 2022

An Optimized Fuzzy Logic Model for Proactive Maintenance

  Authors

Abdelouadoud Kerarmi¹, Assia Kamal-idrissi¹ and Amal El Fallah Seghrouchni^1,2, ¹Mohammed VI Polytechnic University, Morocco, ²Sorbonne University, France

  Abstract

Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely costintensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) thats martly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.

  Keywords

FMECA, Fuzzy Logic, Truth Table, Combinatorial Complexity, Real-time, Industrial fan motor, Knowledge, Big Data, Artificial Intelligence, Proactive maintenance.